Beyond section Q: prioritizing nursing home residents for transition to the community
1 Institute of Gerontology, University of Michigan, 300 North Ingalls, Ann Arbor, MI, USA
2 School of Public Health, University of Michigan, Ann Arbor, MI, USA
3 Geriatric Research, Education, and Clinical Center, Ann Arbor VA Healthcare Center, Ann Arbor, MI, USA
BMC Health Services Research 2012, 12:186 doi:10.1186/1472-6963-12-186Published: 3 July 2012
Nursing Facility Transition (NFT) programs often rely on self-reported preference for discharge to the community, as indicated in the Minimum Data Set (MDS) Section Q, to identify program participants. We examined other characteristics of long-stay residents discharged from nursing facilities by NFT programs, to “flag” similar individuals for outreach in the Money Follows the Person (MFP) initiative.
Three states identified persons who transitioned between 2001 and 2009 with the assistance of a NFT or MFP program. These were used to locate each participant’s MDS 2.0 assessment just prior to discharge and to create a control sample of non-transitioned residents. Logistic regression and Automatic Interactions Detection were used to compare the two groups.
Although there was considerable variation across states in transitionees’ characteristics, a derived “Q + Index” was highly effective in identifying persons similar to those that states had previously transitioned. The Index displays high sensitivity (86.5%) and specificity (78.7%) and identifies 28.3% of all long-stayers for follow-up. The Index can be cross-walked to MDS 3.0 items.
The Q + Index, applied to MDS 3.0 assessments, can identify a population closely resembling persons who have transitioned in the past. Given the US Government’s mandate that states consider all transition requests and the limited staffing available at local contact agencies to address such referrals, this algorithm can also be used to prioritize among persons seeking assistance from local contact agencies and MFP providers.